Which is the solution for 2 (5 + 3x) equal 4 (x + 4) Urgent PLEASE

Mathematics

2 answers:

postnew [5]3 years ago

8 0

Answer:

x = 3

Step-by-step explanation:

Perform the indicated multiplication, using the distribution rule:

10 + 6x = 4x + 16

Gathering the first powers of x on the left and the constants on the right, we get:

2x = 6, or x = 3

murzikaleks [220]3 years ago

7 0

Answer:

2.6

Step-by-step explanation:

2 (5 + 3x) = 4 (x + 4)

multiply 2 by 5 and 3x which would equal 10+6x

now multiply 4 by x and 4 which would equal 4x+16

Now put it in you equation 10+6x = 4x+16

Now add like terms; 4x+6x=10x, 10+16=26

10x=26 Now divided by 10 on both sides

Your answer should be 2.6

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It appears that people who are mildly obese are less active than leaner people. One study looked at the average number of minute

Genrish500 [490]

Answer:

a) 0.0537 = 5.37% probability that the mean number of minutes of daily activity of the 6 mildly obese people exceeds 420 minutes

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Step-by-step explanation:

To solve this question, we need to understand the normal probability distribution and the central limit theorem.

Normal Probability Distribution:

Problems of normal distributions can be solved using the z-score formula.

In a set with mean $\mu$ and standard deviation $\sigma$ , the z-score of a measure X is given by:

$Z = \frac{X - \mu}{\sigma}$

The Z-score measures how many standard deviations the measure is from the mean. After finding the Z-score, we look at the z-score table and find the p-value associated with this z-score. This p-value is the probability that the value of the measure is smaller than X, that is, the percentile of X. Subtracting 1 by the p-value, we get the probability that the value of the measure is greater than X.

Central Limit Theorem

The Central Limit Theorem estabilishes that, for a normally distributed random variable X, with mean $\mu$ and standard deviation $\sigma$ , the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean $\mu$ and standard deviation $s = \frac{\sigma}{\sqrt{n}}$ .